Integrative analysis of multi-omics reveals gene regulatory networks across brain regions from risk variants to phenotypes of Alzheimer’s disease and Covid-19

Regulatory Networks ◽

Target Genes ◽

Disease Risk ◽

Regulatory Elements ◽

Brain Regions ◽

Risk Variants ◽

AbstractBackgroundGenome-wide association studies have found many genetic risk variants associated with Alzheimer’s disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed integrative multi-omics analysis from genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes.MethodFirst, we cluster gene co-expression networks and identify gene modules for various AD phenotypes given population gene expression data. Next, we predict the transcription factors (TFs) that significantly regulate the genes in each module and the AD risk variants (e.g., SNPs) interrupting the TF binding sites on the regulatory elements. Finally, we construct a full gene regulatory network linking SNPs, interrupted TFs, and regulatory elements to target genes for each phenotype. This network thus provides mechanistic insights of gene regulation from disease risk variants to AD phenotypes.ResultsWe applied our analysis to predict the gene regulatory networks in three major AD-relevant regions: hippocampus, dorsolateral prefrontal cortex (DLPFC), and lateral temporal lobe (LTL). These region networks provide a comprehensive functional genomic map linking AD SNPs to TFs and regulatory elements to target genes for various AD phenotypes. Comparative analyses further revealed cross-region-conserved and region-specific regulatory networks. For instance, AD SNPs rs13404184 and rs61068452 disrupt the bindings of TF SPI1 that regulates AD gene INPP5D in the hippocampus and lateral temporal lobe. However, SNP rs117863556 interrupts the bindings of TF REST to regulate GAB2 in the DLPFC only. Furthermore, driven by recent discoveries between AD and Covid-19, we found that many genes from our networks regulating Covid-19 pathways are also significantly differentially expressed in severe Covid patients (ICU), suggesting potential regulatory connections between AD and Covid. Thus, we used the machine learning models to predict severe Covid and prioritized highly predictive genes as AD-Covid genes. We also used Decision Curve Analysis to show that our AD-Covid genes outperform known Covid-19 genes for predicting Covid severity and deciding to send patients to ICU or not. In short, our results provide a deeper understanding of the interplay among multi-omics, brain regions, and AD phenotypes, including disease progression and Covid response. Our analysis is open-source available at https://github.com/daifengwanglab/ADSNPheno.

Integrative multi-omics analyses identify cell-type disease genes and regulatory networks across schizophrenia and Alzheimer’s disease

10.1101/2020.06.11.147314 ◽

2020 ◽

Author(s):

Mufang Ying ◽

Peter Rehani ◽

Panagiotis Roussos ◽

Daifeng Wang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Regulatory Networks ◽

Regulatory Elements ◽

Disease Genes ◽

Omics Data ◽

Cell Type ◽

Cellular Resolution ◽

AbstractStrong phenotype-genotype associations have been reported across brain diseases. However, understanding underlying gene regulatory mechanisms remains challenging, especially at the cellular level. To address this, we integrated the multi-omics data at the cellular resolution of the human brain: cell-type chromatin interactions, epigenomics and single cell transcriptomics, and predicted cell-type gene regulatory networks linking transcription factors, distal regulatory elements and target genes (e.g., excitatory and inhibitory neurons, microglia, oligodendrocyte). Using these cell-type networks and disease risk variants, we further identified the cell-type disease genes and regulatory networks for schizophrenia and Alzheimer’s disease. The celltype regulatory elements (e.g., enhancers) in the networks were also found to be potential pleiotropic regulatory loci for a variety of diseases. Further enrichment analyses including gene ontology and KEGG pathways revealed potential novel cross-disease and disease-specific molecular functions, advancing knowledge on the interplays among genetic, transcriptional and epigenetic risks at the cellular resolution between neurodegenerative and neuropsychiatric diseases. Finally, we summarized our computational analyses as a general-purpose pipeline for predicting gene regulatory networks via multi-omics data.

Cardiac Cell Type-Specific Gene Regulatory Programs and Disease Risk Association

10.1101/2020.09.11.291724 ◽

2020 ◽

Author(s):

James D. Hocker ◽

Olivier B. Poirion ◽

Fugui Zhu ◽

Justin Buchanan ◽

Kai Zhang ◽

...

Keyword(s):

Cardiovascular Disease ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Cell Types ◽

Regulatory Elements ◽

Specific Gene ◽

Dependent Manner ◽

Cardiac Cell ◽

Risk Variants ◽

ABSTRACTBackgroundCis-regulatory elements such as enhancers and promoters are crucial for directing gene expression in the human heart. Dysregulation of these elements can result in many cardiovascular diseases that are major leading causes of morbidity and mortality worldwide. In addition, genetic variants associated with cardiovascular disease risk are enriched within cis-regulatory elements. However, the location and activity of these cis-regulatory elements in individual cardiac cell types remains to be fully defined.MethodsWe performed single nucleus ATAC-seq and single nucleus RNA-seq to define a comprehensive catalogue of candidate cis-regulatory elements (cCREs) and gene expression patterns for the distinct cell types comprising each chamber of four non-failing human hearts. We used this catalogue to computationally deconvolute dynamic enhancers in failing hearts and to assign cardiovascular disease risk variants to cCREs in individual cardiac cell types. Finally, we applied reporter assays, genome editing and electrophysiogical measurements in in vitro differentiated human cardiomyocytes to validate the molecular mechanisms of cardiovascular disease risk variants.ResultsWe defined >287,000 candidate cis-regulatory elements (cCREs) in human hearts at single-cell resolution, which notably revealed gene regulatory programs controlling specific cell types in a cardiac region/structure-dependent manner and during heart failure. We further report enrichment of cardiovascular disease risk variants in cCREs of distinct cardiac cell types, including a strong enrichment of atrial fibrillation variants in cardiomyocyte cCREs, and reveal 38 candidate causal atrial fibrillation variants localized to cardiomyocyte cCREs. Two such risk variants residing within a cardiomyocyte-specific cCRE at the KCNH2/HERG locus resulted in reduced enhancer activity compared to the non-risk allele. Finally, we found that deletion of the cCRE containing these variants decreased KCNH2 expression and prolonged action potential repolarization in an enhancer dosage-dependent manner.ConclusionsThis comprehensive atlas of human cardiac cCREs provides the foundation for not only illuminating cell type-specific gene regulatory programs controlling human hearts during health and disease, but also interpreting genetic risk loci for a wide spectrum of cardiovascular diseases.

Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1517140113 ◽

2016 ◽

Vol 113 (13) ◽

pp. E1835-E1843 ◽

Cited By ~ 8

Author(s):

Mina Fazlollahi ◽

Ivor Muroff ◽

Eunjee Lee ◽

Helen C. Causton ◽

Harmen J. Bussemaker

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Regulatory Networks ◽

Target Genes ◽

Regulation Of Gene Expression ◽

Nonsynonymous Mutation ◽

Gene Regulatory ◽

Genetic Modulators

Regulation of gene expression by transcription factors (TFs) is highly dependent on genetic background and interactions with cofactors. Identifying specific context factors is a major challenge that requires new approaches. Here we show that exploiting natural variation is a potent strategy for probing functional interactions within gene regulatory networks. We developed an algorithm to identify genetic polymorphisms that modulate the regulatory connectivity between specific transcription factors and their target genes in vivo. As a proof of principle, we mapped connectivity quantitative trait loci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between two strains of the yeast Saccharomyces cerevisiae. We identified a nonsynonymous mutation in the DIG2 gene as a cQTL for the transcription factor Ste12p and confirmed this prediction empirically. We also identified three polymorphisms in TAF13 as putative modulators of regulation by Gcn4p. Our method has potential for revealing how genetic differences among individuals influence gene regulatory networks in any organism for which gene expression and genotype data are available along with information on binding preferences for transcription factors.

rSNPBase 3.0: an updated database of SNP-related regulatory elements, element-gene pairs and SNP-based gene regulatory networks

Nucleic Acids Research ◽

10.1093/nar/gkx1101 ◽

2017 ◽

Vol 46 (D1) ◽

pp. D1111-D1116 ◽

Cited By ~ 15

Author(s):

Liyuan Guo ◽

Jing Wang

Keyword(s):

Regulatory Networks ◽

Regulatory Elements ◽

Gene Pairs ◽

Mutations in Transcriptional Regulators Allow Selective Engineering of Signal Integration Logic

mBio ◽

10.1128/mbio.01171-14 ◽

2014 ◽

Vol 5 (3) ◽

Cited By ~ 4

Author(s):

Szabolcs Semsey

Keyword(s):

Regulatory Networks ◽

Regulatory Protein ◽

Point Mutations ◽

Building Blocks ◽

Regulatory Elements ◽

Regulatory Proteins ◽

Single Amino Acid ◽

Signal Integration ◽

ABSTRACT Bacterial cells monitor their environment by sensing a set of signals. Typically, these environmental signals affect promoter activities by altering the activity of transcription regulatory proteins. Promoters are often regulated by more than one regulatory protein, and in these cases the relevant signals are integrated by certain logic. In this work, we study how single amino acid substitutions in a regulatory protein (GalR) affect transcriptional regulation and signal integration logic at a set of engineered promoters. Our results suggest that point mutations in regulatory genes allow independent evolution of regulatory logic at different promoters. IMPORTANCE Gene regulatory networks are built from simple building blocks, such as promoters, transcription regulatory proteins, and their binding sites on DNA. Many promoters are regulated by more than one regulatory input. In these cases, the inputs are integrated and allow transcription only in certain combinations of input signals. Gene regulatory networks can be easily rewired, because the function of cis-regulatory elements and promoters can be altered by point mutations. In this work, we tested how point mutations in transcription regulatory proteins can affect signal integration logic. We found that such mutations allow context-dependent engineering of signal integration logic at promoters, further contributing to the plasticity of gene regulatory networks.

Synthetic gene-regulatory networks in the opportunistic human pathogenStreptococcus pneumoniae

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1920015117 ◽

2020 ◽

Vol 117 (44) ◽

pp. 27608-27619 ◽

Cited By ~ 1

Author(s):

Robin A. Sorg ◽

Clement Gallay ◽

Laurye Van Maele ◽

Jean-Claude Sirard ◽

Jan-Willem Veening

Keyword(s):

Gene Expression ◽

Virulence Factor ◽

Regulatory Networks ◽

Combinatorial Libraries ◽

Regulatory Elements ◽

Synthetic Gene ◽

Expression Control ◽

Gene Expression Control ◽

Streptococcus pneumoniaecan cause disease in various human tissues and organs, including the ear, the brain, the blood, and the lung, and thus in highly diverse and dynamic environments. It is challenging to study how pneumococci control virulence factor expression, because cues of natural environments and the presence of an immune system are difficult to simulate in vitro. Here, we apply synthetic biology methods to reverse-engineer gene expression control inS. pneumoniae. A selection platform is described that allows for straightforward identification of transcriptional regulatory elements out of combinatorial libraries. We present TetR- and LacI-regulated promoters that show expression ranges of four orders of magnitude. Based on these promoters, regulatory networks of higher complexity are assembled, such as logic AND gates and IMPLY gates. We demonstrate single-copy genome-integrated toggle switches that give rise to bimodal population distributions. The tools described here can be used to mimic complex expression patterns, such as the ones found for pneumococcal virulence factors. Indeed, we were able to rewire gene expression of the capsule operon, the main pneumococcal virulence factor, to be externally inducible (YES gate) or to act as an IMPLY gate (only expressed in absence of inducer). Importantly, we demonstrate that these synthetic gene-regulatory networks are functional in an influenza A virus superinfection murine model of pneumonia, paving the way for in vivo investigations of the importance of gene expression control on the pathogenicity ofS. pneumoniae.

Mapping gene regulatory networks of primary CD4+T cells using single-cell genomics and genome engineering

10.1101/678060 ◽

2019 ◽

Cited By ~ 1

Author(s):

Rachel E. Gate ◽

Min Cheol Kim ◽

Andrew Lu ◽

David Lee ◽

Eric Shifrut ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Regulatory Networks ◽

Genome Engineering ◽

Regulatory Elements ◽

Mapping Gene ◽

Gene Regulatory ◽

Novel Interactions

AbstractGene regulatory programs controlling the activation and polarization of CD4+T cells are incompletely mapped and the interindividual variability in these programs remain unknown. We sequenced the transcriptomes of ~160k CD4+T cells from 9 donors following pooled CRISPR perturbation targeting 140 regulators. We identified 134 regulators that affect T cell functionalization, includingIRF2as a positive regulator of Th2polarization. Leveraging correlation patterns between cells, we mapped 194 pairs of interacting regulators, including known (e.g.BATFandJUN) and novel interactions (e.g.ETS1andSTAT6). Finally, we identified 80 natural genetic variants with effects on gene expression, 48 of which are modified by a perturbation. In CD4+T cells, CRISPR perturbations can influencein vitropolarization and modify the effects oftransandcisregulatory elements on gene expression.

AGENT: the Arabidopsis Gene Regulatory Network Tool for Exploring and Analyzing GRNs

10.1101/2021.04.28.441830 ◽

2021 ◽

Author(s):

Vincent Lau ◽

Rachel Woo ◽

Bruno Pereira ◽

Asher Pasha ◽

Eddi Esteban ◽

...

Keyword(s):

Gene Network ◽

Regulatory Networks ◽

Target Genes ◽

Ad Hoc ◽

Arabidopsis Gene ◽

Nitrate Signaling ◽

Multi Level ◽

AbstractGene regulatory networks (GRNs) are complex networks that capture multi-level regulatory events between one or more regulatory macromolecules, such as transcription factors (TFs), and their target genes. Advancements in screening technologies such as enhanced yeast-one-hybrid screens have allowed for high throughput determination of GRNs. However, visualization of GRNs in Arabidopsis has been limited to ad hoc networks and are not interactive. Here, we describe the Arabidopsis GEne Network Tool (AGENT) that houses curated GRNs and provides tools to visualize and explore them. AGENT features include expression overlays, subnetwork motif scanning, and network analysis. We show how to use AGENT’s multiple built-in tools to identify key genes that are involved in flowering and seed development along with identifying temporal multi-TF control of a key transporter in nitrate signaling. AGENT can be accessed at https://bar.utoronto.ca/AGENT.

Identifying noncoding risk variants using disease-relevant gene regulatory networks

Nature Communications ◽

10.1038/s41467-018-03133-y ◽

2018 ◽

Vol 9 (1) ◽

Cited By ~ 15

Author(s):

Long Gao ◽

Yasin Uzun ◽

Peng Gao ◽

Bing He ◽

Xiaoke Ma ◽

...

Keyword(s):

Regulatory Networks ◽

Risk Variants ◽

Gene Regulatory ◽

Relevant Gene

Evolution of Mendelian Dominance in Gene Regulatory Networks Associated With Phenotypic Robustness

10.21203/rs.3.rs-157749/v1 ◽

2021 ◽

Author(s):

Kenji Okubo ◽

Kunihiko Kaneko

Keyword(s):

Regulatory Networks ◽

Target Genes ◽

Classical Case ◽

Mendelian Inheritance ◽

Degree Of Dominance ◽

Gene Expression Dynamics ◽

Phenotypic Robustness ◽

Robustness To Noise ◽

Abstract Background: Mendelian inheritance is a fundamental law of genetics. Considering two alleles in a diploid, a phenotype of a heterotype is dominated by a particular homotype according to the law of dominance. This picture is usually based on simple genotype-phenotype mapping in which one gene regulates one phenotype. However, in reality, some interactions between genes can result in deviation from Mendelian dominance. Result: Here, by using the numerical evolution of diploid gene regulatory networks (GRNs), we discuss whether Mendelian dominance evolves beyond the classical case of one-to-one genotype-phenotype mapping. We examine whether complex genotype-phenotype mapping can achieve Mendelian dominance through the evolution of the GRN with interacting genes. Specifically, we extend the GRN model to a diploid case, in which two GRN matrices are added to give gene expression dynamics, and simulate evolution with meiosis and recombination. Our results reveal that Mendelian dominance evolves even under complex genotype-phenotype mapping. This dominance is achieved via a group of genotypes that differ from each other but have a common phenotype given by the expression of target genes. Calculating the degree of dominance shows that it increases through the evolution, correlating closely with the decrease in phenotypic fluctuations and the increase in robustness to initial noise. This evolution of Mendelian dominance is associated with phenotypic robustness against meiosis-induced genome mixing, whereas sexual recombination arising from the mixing of chromosomes from the parents further enhances dominance and robustness. Owing to this dominance, the robustness to genetic differences increases, while the optimal fitness is sustained up to a large difference between the two genomes. Conclusion: Mendelian dominance is achieved by groups of genotypes that are associated with the increase in phenotypic robustness to noise.